"""simple docstring"""



import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


SCREAMING_SNAKE_CASE__:str									=			"""base_with_context"""



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]				)				)
       __a       =    nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]				)   ,    requires_grad=a				)
       for lyr_num, lyr in enumerate(model.encoders				):
              __a       =    weights[F"layers_{lyr_num}"]
              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]				)				)

              __a       =    ly_weight["attention"]
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T				)				)

              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)

              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T				)				)

       __a       =    nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]				)				)
       return model



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T				)				)

       __a       =    nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]				)   ,    requires_grad=a				)

       for lyr_num, lyr in enumerate(model.encoders				):
              __a       =    weights[F"layers_{lyr_num}"]
              __a       =    ly_weight["attention"]

              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T				)				)
              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]				)				)

              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)

       __a       =    nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]				)				)

       return model



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T				)				)
       __a       =    nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T				)				)

       __a       =    nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]				)   ,    requires_grad=a				)

       __a       =    nn.Parameter(
           torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T				)				)

       for lyr_num, lyr in enumerate(model.decoders				):
              __a       =    weights[F"layers_{lyr_num}"]
              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]				)				)

              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T				)				)

              __a       =    ly_weight["self_attention"]
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T				)				)

              __a       =    ly_weight["MultiHeadDotProductAttention_0"]
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T				)				)
              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]				)				)

              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)
              __a       =    nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T				)				)
              __a       =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T				)				)

       __a       =    nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]				)				)

       __a       =    nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T				)				)

       return model



def 	_lowerCamelCase(	a				):
       __a       =    checkpoints.load_tax_checkpoint(args.checkpoint_path				)
       __a       =    jnp.tree_util.tree_map(onp.array   ,    a				)

       __a       =    [
           "from __gin__ import dynamic_registration",
           "from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
           "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
           "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
       ]

       __a       =    os.path.join(args.checkpoint_path   ,    ".."   ,    "config.gin"				)
       __a       =    inference.parse_training_gin_file(a   ,    a				)
       __a       =    inference.InferenceModel(args.checkpoint_path   ,    a				)

       __a       =    DDPMScheduler(beta_schedule="squaredcos_cap_v2"   ,    variance_type="fixed_large"				)

       __a       =    SpectrogramNotesEncoder(
           max_length=synth_model.sequence_length["inputs"]   ,    vocab_size=synth_model.model.module.config.vocab_size   ,    d_model=synth_model.model.module.config.emb_dim   ,    dropout_rate=synth_model.model.module.config.dropout_rate   ,    num_layers=synth_model.model.module.config.num_encoder_layers   ,    num_heads=synth_model.model.module.config.num_heads   ,    d_kv=synth_model.model.module.config.head_dim   ,    d_ff=synth_model.model.module.config.mlp_dim   ,    feed_forward_proj="gated-gelu"   ,    )

       __a       =    SpectrogramContEncoder(
           input_dims=synth_model.audio_codec.n_dims   ,    targets_context_length=synth_model.sequence_length["targets_context"]   ,    d_model=synth_model.model.module.config.emb_dim   ,    dropout_rate=synth_model.model.module.config.dropout_rate   ,    num_layers=synth_model.model.module.config.num_encoder_layers   ,    num_heads=synth_model.model.module.config.num_heads   ,    d_kv=synth_model.model.module.config.head_dim   ,    d_ff=synth_model.model.module.config.mlp_dim   ,    feed_forward_proj="gated-gelu"   ,    )

       __a       =    TaFilmDecoder(
           input_dims=synth_model.audio_codec.n_dims   ,    targets_length=synth_model.sequence_length["targets_context"]   ,    max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time   ,    d_model=synth_model.model.module.config.emb_dim   ,    num_layers=synth_model.model.module.config.num_decoder_layers   ,    num_heads=synth_model.model.module.config.num_heads   ,    d_kv=synth_model.model.module.config.head_dim   ,    d_ff=synth_model.model.module.config.mlp_dim   ,    dropout_rate=synth_model.model.module.config.dropout_rate   ,    )

       __a       =    load_notes_encoder(ta_checkpoint["target"]["token_encoder"]   ,    a				)
       __a       =    load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]   ,    a				)
       __a       =    load_decoder(ta_checkpoint["target"]["decoder"]   ,    a				)

       __a       =    OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"				)

       __a       =    SpectrogramDiffusionPipeline(
           notes_encoder=a   ,    continuous_encoder=a   ,    decoder=a   ,    scheduler=a   ,    melgan=a   ,    )
       if args.save:
              pipe.save_pretrained(args.output_path				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()

   parser.add_argument("""--output_path""", default=None, type=str, required=True, help="""Path to the converted model.""")
   parser.add_argument(
       """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
   )
   parser.add_argument(
       """--checkpoint_path""",
       default=F'''{MODEL}/checkpoint_500000''',
       type=str,
       required=False,
       help="""Path to the original jax model checkpoint.""",
   )
   SCREAMING_SNAKE_CASE__:Optional[int]									=			parser.parse_args()

   main(args)

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    (list, tuple)				) and isinstance(videos[0]   ,    (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(a   ,    (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(a				):
              return [[videos]]

       raise ValueError(F"Could not make batched video from {videos}"				)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)
        __a       =    size if size is not None else {"shortest_edge": 256}
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else {"height": 224, "width": 224}
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        __a       =    do_resize
        __a       =    size
        __a       =    do_center_crop
        __a       =    crop_size
        __a       =    resample
        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    offset
        __a       =    do_normalize
        __a       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        if "shortest_edge" in size:
               __a       =    get_resize_output_image_size(lowerCamelCase    ,		size["shortest_edge"]    ,		default_to_square=lowerCamelCase						)
        elif "height" in size and "width" in size:
               __a       =    (size["height"], size["width"])
        else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
        return resize(lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase						)
        if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
        return center_crop(lowerCamelCase    ,		size=(size["height"], size["width"])    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    image.astype(np.floataa						)
        if offset:
               __a       =    image - (scale / 2)
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        return normalize(lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		):
        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        __a       =    to_numpy_array(lowerCamelCase						)

        if do_resize:
               __a       =    self.resize(image=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase						)

        if do_center_crop:
               __a       =    self.center_crop(lowerCamelCase    ,		size=lowerCamelCase						)

        if do_rescale:
               __a       =    self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase    ,		offset=lowerCamelCase						)

        if do_normalize:
               __a       =    self.normalize(image=lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase						)

        __a       =    to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						)
        return image






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_resize if do_resize is not None else self.do_resize
        __a       =    resample if resample is not None else self.resample
        __a       =    do_center_crop if do_center_crop is not None else self.do_center_crop
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    offset if offset is not None else self.offset
        __a       =    do_normalize if do_normalize is not None else self.do_normalize
        __a       =    image_mean if image_mean is not None else self.image_mean
        __a       =    image_std if image_std is not None else self.image_std

        __a       =    size if size is not None else self.size
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else self.crop_size
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        __a       =    make_batched(lowerCamelCase						)

        __a       =    [
            [
                self._preprocess_image(
                    image=lowerCamelCase    ,		do_resize=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		do_center_crop=lowerCamelCase    ,		crop_size=lowerCamelCase    ,		do_rescale=lowerCamelCase    ,		rescale_factor=lowerCamelCase    ,		offset=lowerCamelCase    ,		do_normalize=lowerCamelCase    ,		image_mean=lowerCamelCase    ,		image_std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		)
                for img in video
            ]
            for video in videos
        ]

        __a       =    {"pixel_values": videos}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)
